Build and train AI & machine learning models, prepare and analyze data – all in a flexible, hybrid cloud environment
Analyze data using RStudio, Jupyter, and Python in a configured, collaborative environment that includes IBM value-adds, such as managed Spark. IBM Watson Studio offers all this functionality, including:
- a single workspace for your tools
- a searchable, growing community
- shareable, collaborative projects
- supporting content, where you need it
The IBM Watson Visual Recognition service uses deep learning algorithms to analyze images for scenes, objects, faces, and other content. IBM Watson Studio provides a collaborative environment in the cloud where you can work with your images and your Visual Recognition custom models.
Product Link: https://www.ibm.com/cloud/watson-studio
You're just a few steps away from getting started with IBM Watson™.
Create an account on IBM Cloud to try Watson services for free with no time restrictions: Sign up for free. You'll receive an email to confirm and activate your account.
After you activate your account and log in, click Browse Services from the Watson console. Select the Watson Visual Recognition Service. Create an instance of the service for free.
- Create an instance of the service:
- Go to the Visual Recognition page in the catalog.
- Sign up for a free IBM Cloud account or log in.
- Click Create.
- Copy the credentials to authenticate to your service instance:
- On the Manage page, click Show Credentials.
- Copy the API Key and URL values.
- Make sure that you have the curl command.
-
Test whether curl is installed
- Run the following command on the command line. If the output lists the curl version with SSL support, then you should be all set:
$ curl -V- If necessary, install a version with SSL enabled from curl.haxx.se. Add the location of the file to your PATH environment variables if you want to run curl from any command-line location.
-
NOTE: IBM Cloud Dedicated plans authenticate by using -u "{username}:{password}". Use the username and password values for your specific instance.
- We are going to classify the following image using a general pre-trained General Model by IBM.
To classify the image above. Run the following command and replace {apikey} and {url} with the service credentials from earlier. Also, replace {img_url} with the url of the image above(https://tinyurl.com/vory3jd):
$ curl -u "apikey:{apikey}" "{url}/v3/classify?url={img_url}&version=2019-12-10"
Response:
{
"images": [
{
"classifiers": [
{
"classifier_id": "default",
"name": "default",
"classes": [
{
"class": "retriever dog",
"score": 0.969,
"type_hierarchy": "/animal/domestic animal/dog/retriever dog"
},
{
"class": "dog",
"score": 0.982
},
{
"class": "domestic animal",
"score": 0.982
},
{
"class": "animal",
"score": 0.982
},
{
"class": "golden retriever dog",
"score": 0.856,
"type_hierarchy": "/animal/domestic animal/dog/retriever dog/golden retriever dog"
},
{
"class": "sporting dog",
"score": 0.5,
"type_hierarchy": "/animal/domestic animal/dog/sporting dog"
},
{
"class": "light brown color",
"score": 0.995
}
]
}
],
"source_url": "https://tinyurl.com/vory3jd",
}
],
"images_processed": 1,
"custom_classes": 0
}
- Watson Visual Recognition Studio includes a built-in Food model that might be more accurate for your images with food items.
- Using a similar command prompt issue a call to classify this image against the Food model:
$ curl -u "apikey:{apikey}" -F "classifier_ids=food" "{url}/v3/classify?url={img_url}&version=2019-12-10"
Response:
{
"images": [
{
"classifiers": [
{
"classifier_id": "food",
"name": "food",
"classes": [
{
"class": "grape",
"score": 0.665,
"type_hierarchy": "/fruit/grape"
},
{
"class": "fruit",
"score": 0.873
},
{
"class": "simple fruit",
"score": 0.5,
"type_hierarchy": "/fruit/berry/simple fruit"
},
{
"class": "berry",
"score": 0.538
}
]
}
],
"source_url": "https://tinyurl.com/wvph7pd",
}
],
"images_processed": 1,
"custom_classes": 0
}